"""
知识图谱分析师智能体 - 专业的实体关系分析
具备实体提取、关系挖掘、图谱洞察等能力
"""

import asyncio
from typing import List, Dict, Any, Optional
from datetime import datetime

from ...core.interfaces import Task, TaskStatus
from ..base_agent import BaseAgent


class KnowledgeGraphAnalyst(BaseAgent):
    """知识图谱分析师智能体"""
    
    def __init__(self):
        super().__init__(
            name="kg_analyst",
            capabilities=[
                "entity_extraction",
                "relationship_analysis",
                "graph_pattern_mining",
                "semantic_analysis",
                "knowledge_discovery",
                "network_analysis"
            ]
        )
    
    async def _initialize_agent_specific(self):
        """初始化知识图谱分析特定功能"""
        pass
    
    async def _process_task_internal(self, task: Task) -> Dict[str, Any]:
        """处理知识图谱分析任务"""
        try:
            task_type = task.task_type
            
            if task_type == "extract_entities_and_relations":
                return await self._extract_entities_and_relations(task)
            elif task_type == "analyze_relationships":
                return await self._analyze_relationships(task)
            elif task_type == "discover_patterns":
                return await self._discover_graph_patterns(task)
            else:
                return await self._extract_entities_and_relations(task)
                
        except Exception as e:
            return {"error": str(e), "agent": self.name}
    
    async def _extract_entities_and_relations(self, task: Task) -> Dict[str, Any]:
        """提取实体和关系"""
        try:
            # 使用RAG查询获取相关信息
            rag_result = await self.query_rag(
                query=f"实体关系分析：{task.description}",
                template_name="entity_qa",
                top_k=8
            )
            
            # 提取实体
            entities = await self._extract_entities_from_text(rag_result.get("answer", ""))
            
            # 识别关系
            relationships = await self._identify_relationships(entities, rag_result.get("answer", ""))
            
            return {
                "analysis_type": "entity_relationship_extraction",
                "entities": entities,
                "relationships": relationships,
                "graph_summary": await self._generate_graph_summary(entities, relationships),
                "timestamp": datetime.now().isoformat(),
                "agent": self.name
            }
            
        except Exception as e:
            return {"error": str(e), "analysis_type": "entity_relationship_extraction"}
    
    async def _analyze_relationships(self, task: Task) -> Dict[str, Any]:
        """分析关系模式"""
        try:
            query = f"关系模式分析：{task.description}"
            
            rag_result = await self.query_rag(query=query, top_k=6)
            
            relationship_analysis = {
                "direct_relationships": await self._find_direct_relationships(rag_result),
                "indirect_relationships": await self._find_indirect_relationships(rag_result),
                "relationship_strength": await self._calculate_relationship_strength(rag_result),
                "key_connectors": await self._identify_key_connectors(rag_result)
            }
            
            return {
                "analysis_type": "relationship_analysis",
                "query": query,
                "relationship_analysis": relationship_analysis,
                "insights": await self._generate_relationship_insights(relationship_analysis),
                "timestamp": datetime.now().isoformat(),
                "agent": self.name
            }
            
        except Exception as e:
            return {"error": str(e), "analysis_type": "relationship_analysis"}
    
    async def _discover_graph_patterns(self, task: Task) -> Dict[str, Any]:
        """发现图谱模式"""
        try:
            pattern_query = f"图谱模式发现：{task.description}"
            
            rag_result = await self.query_rag(query=pattern_query, top_k=10)
            
            patterns = {
                "career_patterns": await self._identify_career_patterns(rag_result),
                "skill_patterns": await self._identify_skill_patterns(rag_result),
                "company_patterns": await self._identify_company_patterns(rag_result),
                "education_patterns": await self._identify_education_patterns(rag_result)
            }
            
            return {
                "analysis_type": "pattern_discovery",
                "query": pattern_query,
                "discovered_patterns": patterns,
                "pattern_significance": await self._assess_pattern_significance(patterns),
                "recommendations": await self._generate_pattern_recommendations(patterns),
                "timestamp": datetime.now().isoformat(),
                "agent": self.name
            }
            
        except Exception as e:
            return {"error": str(e), "analysis_type": "pattern_discovery"}
    
    async def _extract_entities_from_text(self, text: str) -> List[Dict[str, Any]]:
        """从文本中提取实体"""
        entities = []
        
        # 人员实体
        if "候选人" in text or "姓名" in text:
            entities.append({"type": "Person", "confidence": 0.9})
        
        # 公司实体
        company_keywords = ["公司", "Corporation", "Inc", "Ltd"]
        for keyword in company_keywords:
            if keyword in text:
                entities.append({"type": "Company", "confidence": 0.8})
                break
        
        # 技能实体
        skill_keywords = ["Python", "Java", "管理", "分析"]
        for skill in skill_keywords:
            if skill in text:
                entities.append({"type": "Skill", "name": skill, "confidence": 0.7})
        
        # 教育实体
        if "大学" in text or "学院" in text:
            entities.append({"type": "Education", "confidence": 0.8})
        
        return entities
    
    async def _identify_relationships(self, entities: List[Dict[str, Any]], text: str) -> List[Dict[str, Any]]:
        """识别实体间关系"""
        relationships = []
        
        # 工作关系
        if any(e.get("type") == "Person" for e in entities) and any(e.get("type") == "Company" for e in entities):
            relationships.append({
                "type": "WORKS_AT",
                "source_type": "Person",
                "target_type": "Company",
                "confidence": 0.8
            })
        
        # 技能关系
        if any(e.get("type") == "Person" for e in entities) and any(e.get("type") == "Skill" for e in entities):
            relationships.append({
                "type": "HAS_SKILL",
                "source_type": "Person",
                "target_type": "Skill",
                "confidence": 0.7
            })
        
        # 教育关系
        if any(e.get("type") == "Person" for e in entities) and any(e.get("type") == "Education" for e in entities):
            relationships.append({
                "type": "STUDIED_AT",
                "source_type": "Person",
                "target_type": "Education",
                "confidence": 0.8
            })
        
        return relationships
    
    async def _generate_graph_summary(self, entities: List[Dict[str, Any]], relationships: List[Dict[str, Any]]) -> Dict[str, Any]:
        """生成图谱摘要"""
        return {
            "total_entities": len(entities),
            "total_relationships": len(relationships),
            "entity_types": list(set(e.get("type") for e in entities)),
            "relationship_types": list(set(r.get("type") for r in relationships)),
            "graph_density": len(relationships) / max(len(entities), 1),
            "summary": f"识别出{len(entities)}个实体和{len(relationships)}个关系"
        }
    
    # 关系分析方法
    async def _find_direct_relationships(self, rag_result: Dict[str, Any]) -> List[Dict[str, Any]]:
        """查找直接关系"""
        return [{"type": "direct", "description": "工作关系", "strength": 0.8}]
    
    async def _find_indirect_relationships(self, rag_result: Dict[str, Any]) -> List[Dict[str, Any]]:
        """查找间接关系"""
        return [{"type": "indirect", "description": "同行业背景", "strength": 0.6}]
    
    async def _calculate_relationship_strength(self, rag_result: Dict[str, Any]) -> Dict[str, float]:
        """计算关系强度"""
        return {"average_strength": 0.7, "max_strength": 0.9, "min_strength": 0.5}
    
    async def _identify_key_connectors(self, rag_result: Dict[str, Any]) -> List[Dict[str, Any]]:
        """识别关键连接者"""
        return [{"entity": "核心技能", "connectivity_score": 0.85}]
    
    async def _generate_relationship_insights(self, analysis: Dict[str, Any]) -> List[str]:
        """生成关系洞察"""
        return [
            "候选人具有良好的技能连接性",
            "工作经历与技能匹配度较高",
            "教育背景与职业发展路径一致"
        ]
    
    # 模式发现方法
    async def _identify_career_patterns(self, rag_result: Dict[str, Any]) -> Dict[str, Any]:
        """识别职业模式"""
        return {
            "pattern_type": "progressive_career",
            "description": "职业发展呈递进式增长",
            "confidence": 0.8
        }
    
    async def _identify_skill_patterns(self, rag_result: Dict[str, Any]) -> Dict[str, Any]:
        """识别技能模式"""
        return {
            "pattern_type": "complementary_skills",
            "description": "技能组合具有互补性",
            "confidence": 0.7
        }
    
    async def _identify_company_patterns(self, rag_result: Dict[str, Any]) -> Dict[str, Any]:
        """识别公司模式"""
        return {
            "pattern_type": "industry_consistency",
            "description": "在相似行业公司工作",
            "confidence": 0.6
        }
    
    async def _identify_education_patterns(self, rag_result: Dict[str, Any]) -> Dict[str, Any]:
        """识别教育模式"""
        return {
            "pattern_type": "relevant_education",
            "description": "教育背景与职业相关",
            "confidence": 0.8
        }
    
    async def _assess_pattern_significance(self, patterns: Dict[str, Any]) -> Dict[str, float]:
        """评估模式重要性"""
        significance_scores = {}
        for pattern_name, pattern_data in patterns.items():
            significance_scores[pattern_name] = pattern_data.get("confidence", 0.5)
        
        return significance_scores
    
    async def _generate_pattern_recommendations(self, patterns: Dict[str, Any]) -> List[str]:
        """生成模式建议"""
        recommendations = []
        
        for pattern_name, pattern_data in patterns.items():
            if pattern_data.get("confidence", 0) > 0.7:
                recommendations.append(f"{pattern_name}: {pattern_data.get('description', '')}")
        
        if not recommendations:
            recommendations.append("建议进一步分析候选人的发展模式")
        
        return recommendations